
#All the necessary packages we used for this project
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.patches as mpatches
import matplotlib.cm as cm
import plotly.express as px
import seaborn as sns
from ttictoc import tic,toc
import networkx as nx
import seaborn as sns
import re as re
from networkx.algorithms.community.centrality import girvan_newman
from networkx.algorithms.centrality import edge_betweenness_centrality
from networkx.algorithms.community.quality import modularity
import community.community_louvain
import operator
from fa2l import force_atlas2_layout
from wordcloud import WordCloud
import warnings
from collections import Counter


# Packages for clustering
import os
from sklearn.preprocessing import StandardScaler
from scipy.cluster.hierarchy import dendrogram
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import SpectralClustering


df = pd.read_csv("data/nutrition.csv", index_col = 0)
df.head()


# It is compulsory to get rid of the label of the unit in each data element.
# We require either int, float or bool rather than string to operate correctly.
def obtain_val(string):
  val = re.findall('\d*\.?\d+',string)
  return "".join(val)

df.drop(['serving_size'], axis=1)
#df['total_fat']=df['total_fat'].apply(lambda x: obtain_val(x)).astype(float)
df2 = df.filter(['name'], axis=1)
df = df.drop(columns=['name'])

df = df.astype(str)

#df = df.replace("",0)
df = df.applymap(obtain_val)
df = df.mask(df == '')
df = df.astype(float)
df2 = df2.join(df)

df2.head()


current_path = os.getcwd()

# Leave out columns/features that are not relevant for saturated_fat prediction
# Some research has been carried out to figure it out. Those features that keep a higher qualitative correlation with fats and the "critic nutrients", i.e., sodium and sugar.

# Features to keep: calories, total_fat, cholesterol, sodium, carbohydrate, glucose, saturated_fatty_acids, monounsaturated_fatty_acids, polyunsaturated_fatty_acids, fatty_acids_total_trans, water  
cols_to_keep = ["calories", "total_fat", "cholesterol", "sodium", "carbohydrate", "glucose", "sugars", "saturated_fatty_acids", "monounsaturated_fatty_acids", "polyunsaturated_fatty_acids", "fatty_acids_total_trans", "water"]
df_clust = df2.loc[:,cols_to_keep]

df_clust.head(10)


warnings.filterwarnings("ignore")
# Plot 3d scatter plot with 3 of the previous pre-selected features (e.g., total_fat, calories, sugars)
fig = plt.figure(figsize=(13,10)).gca(projection = "3d")
fig.scatter(df_clust["total_fat"], df_clust["calories"], df_clust["sugars"])
fig.set_xlabel("Total Fat")
fig.set_ylabel("Calories")
fig.set_zlabel("Sugars")
plt.show()


# As a visual reference, create the 2d subplots from each pair of the previous labels.
#fig = plt.figure(figsize=(25,20))
fig, axs = plt.subplots(3,figsize=(13, 10))

axs[0].scatter(x=df_clust["total_fat"], y=df_clust["sugars"], marker="o")
axs[0].set_xlabel("Total Fat")
axs[0].set_ylabel("Sugars")

axs[1].scatter(x=df_clust["total_fat"], y=df_clust["calories"], marker="o")
axs[1].set_xlabel("Total Fat")
axs[1].set_ylabel("Calories")

axs[2].scatter(x=df_clust["sugars"], y=df_clust["calories"], marker="o")
axs[2].set_xlabel("Sugars")
axs[2].set_ylabel("Calories")

plt.show()


# IMPORTANT: Before applying any clustering algorithm, normalizing the variables/features is required.
# For instance, calories and sugars scales are completely different. It might incur in future issues.

scaler = StandardScaler()
X = df_clust

scaled_df = pd.DataFrame(scaler.fit_transform(X), columns = X.columns)

# Let's show the usual statistical features of each variable to ensure the normalisation.
scaled_df.describe()

# See that the dataframe is normalised since they all have std = 1.


# Hierarchical Clustering: Dendogram
# No previous expectation of potential number of clusters

# Function taken from O'Reilly interactive Lab by Tobias Zwingmann
def dendogram_plot(md, **kwargs):
  count = np.zeros(md.children_.shape[0])
  n_samples = len(md.labels_)
  for (i, merge) in enumerate(md.children_):
      count_sum = 0
      for child_idx in merge:
          if child_idx < n_samples:
              count_sum += 1
          else:
              count_sum += count[child_idx - n_samples]
      count[i] = count_sum
  linkage_matrix = np.column_stack([md.children_,md.distances_,count]).astype(float)
  # Plot dendogram
  dendrogram(linkage_matrix, **kwargs)

X = scaled_df

# Model definition by using Agglomerative Clustering from sklearn. 
# No references of the desired number of clusters.
md = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

# Finally, let's create a dendogram by fitting our reduced dataframe.
clustered_model = md.fit(X)


# Representation of the fitted dendrogram
fig = plt.figure(figsize=(12,10))
plt.title("Dendogram - Depth: 3")
dendogram_plot(clustered_model, truncate_mode ='level', p=3)
plt.xlabel("Number of ingredients/elements per node")
plt.savefig('Clustering_Dendogram.png')
plt.show()

# COMMENT: In the dendrogram, numbers that are not in parentesis represent single values. This means, the id number of that observation.


# QUESTION: Where do we cut the dendogram so we get the clusters with the highest separation among them? 
# If we separate out data in 4 clusters (i.e,. approx. height of 125), we obtain the following number of observations per cluster:
#   Cluster 1 (yellow-left): 13
#   Cluster 2 (yellow-right): 6665
#   Cluster 3 (blue-left): 357
#   Cluster 4 (blue-right): 1754

# However, this segmentation would not be descriptive enough since most of our observations would be added to Cluster 2. 
# Therefore, let's choose a more accurate number of clusters: 7 (height 80)
#   Cluster 1 (yellow-left): 13
#   Cluster 2 (yellow-center): 2737
#   Cluster 3 (yellow-right): 3930
#   Cluster 4 (blue-left): 357
#   Cluster 5 (red-left): 8
#   Cluster 6 (red-center): 37
#   Cluster 7 (red-right): 1709

# Thus, we are trying to maximize distances among clusters and disparity.


# Set a specific number of clusters based on a distance_threshold
data_fitting = AgglomerativeClustering(distance_threshold=90,n_clusters=None).fit(scaled_df)

# The unique count for each cluster is:
np.unique(data_fitting.labels_,return_counts=True)

# Matches our expectations.

(array([0, 1, 2, 3, 4, 5, 6]),
array([1709, 3930,  357,   13,    8,   37, 2735]))


# For later use, we will add the cluster label to each ingredient in the non-normalized dataframes.
df_clust['Cluster num.'] = data_fitting.labels_.astype(str)
df2['Cluster num.'] = data_fitting.labels_.astype(str)


# Let's replot the previous scatter plots showing the our new clusters.
sns.lmplot(x = 'total_fat', y='sugars', data=df_clust, hue='Cluster num.', fit_reg=False)
plt.show()


# Same between sugars and calories
sns.lmplot(x = 'sugars', y='calories', data=df_clust, hue='Cluster num.', fit_reg=False)
plt.show()


# Finally, we will assign the mean saturated_fat value from each cluster to the ingredients with non-defined value (NaN)
# NOTE: Pandas excludes NaN values when creating the mean.

mean_satFat = df2.groupby('Cluster num.')['saturated_fat'].mean()
mean_satFat

Cluster num.
0     4.362579
1     4.089225
2    18.600280
3     4.815385
4     2.725000
5     2.311111
6     0.767811
Name: saturated_fat, dtype: float64


df2["saturated_fat"] = np.where(pd.isna(df2["saturated_fat"]), mean_satFat[df2["Cluster num."]], df2["saturated_fat"])

df2.head(10)


# Drop cluster number column
df = df2.drop(columns=["Cluster num."])
df.head(5)
# Save new dataframe
df.to_csv("cleaned_data.csv")


df=pd.read_csv('data/cleaned_data.csv',index_col=0)
df=df.set_index('name')

# copy the data
df_notnorm=df.copy()
df_norm = df.copy()

# apply normalization techniques
for column in df_norm.columns:
  df_norm[column] = (df_norm[column] - df_norm[column].min()) / (df_norm[column].max() - df_norm[column].min())    
  
df_norm.head()


#NetworkX Graph to store the network
percentage=0.15
criteria=int(percentage*8789)

df=df_norm.copy()

F= nx.Graph()
for el in list(df):
  F.add_node(el,Role='Feature')
for el in df.index:
  F.add_node(el,Role='Aliment')
  
print("There are " +str(F.number_of_nodes())+" nodes in our network.")


for f in df.columns:
  sorted_df=df.sort_values(by=[f],ascending=False).iloc[:criteria,0]
  for idx,el in enumerate(sorted_df.index):
      F.add_weighted_edges_from([(f,el,sorted_df[idx])])

There are 8863 nodes in our network.


print("There are " +str(F.number_of_edges())+" edges in our network.")

There are 97532 edges in our network.


nodes_d0 = [node for node,degree in dict(F.degree()).items() if degree == 0]
print(len(nodes_d0))

143


sF=F.copy()
sF.remove_nodes_from(nodes_d0)


print('Number of nodes:',sF.number_of_nodes())
print('NUmber of edges:',sF.number_of_edges())

Number of nodes: 8720
NUmber of edges: 97532


color_map=[]
degree=[]
for node in F.nodes:
  if F.nodes[node]['Role']=='Feature':
      color_map.append('#157B87')
      degree.append(300)
  else:
      color_map.append('#9B149A')
      degree.append(F.degree(node))


positions = force_atlas2_layout(F,
                              iterations=60,
                              pos_list=None,
                              node_masses=None,
                              outbound_attraction_distribution=False,
                              lin_log_mode=False,
                              prevent_overlapping=False,
                              edge_weight_influence=2.0,

                              jitter_tolerance=1.0,
                              barnes_hut_optimize=True,
                              barnes_hut_theta=1.2,

                              scaling_ratio=0.1,
                              strong_gravity_mode=False,
                              multithread=False,
                              gravity=10.0)

options={
  'node_color':color_map,
  'node_size':degree,
  'width':0.4,
  'alpha':0.9
}


plt.figure(figsize=(30, 15))
plt.title('Nutritional network')
fig = plt.gcf()
nx.draw(F, positions, **options)

fig.set_size_inches(20, 15)
plt.savefig('network')


hist_dist={}
nodesA = [x for x,y in F.nodes(data=True) if y['Role']=='Aliment']
for ali in nodesA:
  hist_dist[ali]=F.degree[ali]
  
count1,bins1=np.histogram(list(hist_dist.values()),bins=np.arange(0,51))
plt.bar(bins1[:-1], count1,color='darkorange')
plt.title('Distribution of degrees of the Aliments Nodes of our network',fontsize=10)
plt.xlabel('Degree',fontsize=12,fontweight='bold')
plt.ylabel('Count',fontsize=12)
plt.savefig('Histogram')
plt.show()


print('The average degree of the aliment nodes',np.round(np.mean(list(hist_dist.values())),3))

The average degree of the aliment nodes 11.097


sorted_hist = sorted(hist_dist.items(), key=lambda x: x[1], reverse=True)
for i in range(1,11):
  print(i,'-',sorted_hist[i][0],'is rich in',sorted_hist[i][1],'features.')

1 - Egg, dried, yolk is rich in 49 features.
2 - Egg Mix, USDA Commodity is rich in 45 features.
3 - Spices, dried, parsley is rich in 45 features.
4 - Beef, pan-fried, cooked, liver, variety meats and by-products is rich in 44 features.
5 - Beef, braised, cooked, liver, variety meats and by-products is rich in 44 features.
6 - Egg, glucose reduced, stabilized, dried, whole is rich in 43 features.
7 - Veal, pan-fried, cooked, liver, variety meats and by-products is rich in 43 features.
8 - Seeds, without salt, roasted, pumpkin and squash seed kernels is rich in 43 features.
9 - Spices, ground, mustard seed is rich in 42 features.
10 - Seeds, dried, pumpkin and squash seed kernels is rich in 42 features.


aliments_0=0
for k,v in hist_dist.items():
  if v==0:
      aliments_0+=1
print('There are',aliments_0,'with 0 edges.')

There are 143 with 0 edges.


def Girvan_Newman(G,num_communities=6):
  communities=1
  while communities<num_communities:
      d_s=nx.shortest_path(G)
      s=[]
      for i in d_s.values():
          for j in i.values():
              if len(j)>1:
                  s.append(j)
      edges=[]
      for el in s:
          if len(el)>2:
              l=len(el);c=0
              while c<l-1:
                  edges.append(str(el[c:c+2]));c+=1
          else:
              edges.append(str(el))
      counts=Counter(edges)
      d={}
      for el in list(G.edges()):
          idx=str(list(el))
          d[el]=counts[idx]

      # Remove edge with highest centrality
      idx_max=list(d.values()).index(max(d.values()))
      max_edge=list(d.keys())[idx_max]
      G.remove_edges_from([max_edge])
      communities+=1
  return G


#Using a subgraph to apply the handed-coded Girwan_Newman
sub_graph=sF.copy()
remove = [node for node,degree in dict(sub_graph.degree()).items() if degree <45]
sub_graph.remove_nodes_from(remove)
remove2 = [node for node,degree in dict(sub_graph.degree()).items() if degree ==0]
sub_graph.remove_nodes_from(remove2)
print('Initial subgraph with',len(sub_graph.nodes()),'nodes and',len(sub_graph.edges()),'edges.')

nx.draw(sub_graph, with_labels=True,font_size=8)

Initial subgraph with 66 nodes and 188 edges.


P=Girvan_Newman(sub_graph)
print('New graph using Girvan_Newman Alorithm',P,'.')
nx.draw(P, with_labels=True,font_size=8)

New graph using Girvan_Newman Alorithm Graph with 66 nodes and 183 edges .


partition = community.community_louvain.best_partition(sF)
print('Number of communities found: ', len(np.unique(list(partition.values()))))
mod = community.community_louvain.modularity(partition, sF)
print("The modularity value is:","{:.3f}".format(mod))

Number of communities found:  43
The modularity value is: 0.380


def get_keys_from_value(d, val):
  return [k for k, v in d.items() if v == val]


#Delete the communities that have just one node:
v=list(partition.values())
p=partition
a_remove=[]
for i in (list(range(0,len(np.unique(list(partition.values())))))):
  x=list(partition.values()).count(i) 
  if x==1:
      v.remove(i)
      a=get_keys_from_value(p, i)
      a_remove.append(a[0])
      del p[a[0]]
      
      
communities_d=dict.fromkeys(list(range(0,len(np.unique(v)))))


sF2=sF.copy()
sF2.remove_nodes_from(a_remove)


# We compute the number of songs in every community
for i in range(0,len(np.unique(v))):
  l=[]
  for el in partition:
      if partition[el]==list(np.unique(v))[i]:
          l.append(el)
  communities_d[i]=l
  
size_communities=[len(x) for x in communities_d.values()]
for ind,j in enumerate(size_communities):
   print('Community',ind,'with:',j,'nodes.')

Community 0 with: 1673 nodes.
Community 1 with: 1971 nodes.
Community 2 with: 2478 nodes.
Community 3 with: 2559 nodes.


#Delete the communities that have just one node to plot histogram:
sub_communities_v=[]

for i in range(0,len(size_communities)):
  sub_communities_v=sub_communities_v+[i]*size_communities[i]


count,bins= np.histogram(sub_communities_v,bins=[0,1,2,3,4])
plt.bar(bins[:-1], count,color='#0877aa');plt.title('Community Size Distribution',fontsize=13)
plt.xlabel('Community',fontsize=12); plt.ylabel('Counts',fontsize=12)
plt.savefig('size_distribution')
plt.show()


communities_named={}
for el in communities_d:
  degrees=[sF2.degree(node) for node in communities_d[el]]
  communities_named[communities_d[el][degrees.index(max(degrees))]]=communities_d[el]

for ind, j in enumerate(communities_named.keys()):
  print('Community',ind,'with',"\033[1m" + j + "\033[0m",'as the node with the maximum degree.')

Community 0 with calories as the node with the maximum degree.
Community 1 with cholesterol as the node with the maximum degree.
Community 2 with folate as the node with the maximum degree.
Community 3 with carotene_beta as the node with the maximum degree.


node_community = [node[1] for node in p.items()]
pCol = {i: list(np.random.random(size=3)) for i in set(node_community)}


positions = force_atlas2_layout(sF2,
                              iterations=100,
                              pos_list=None,
                              node_masses=None,
                              outbound_attraction_distribution=False,
                              lin_log_mode=False,
                              prevent_overlapping=False,
                              edge_weight_influence=2.0,

                              jitter_tolerance=1.0,
                              barnes_hut_optimize=True,
                              barnes_hut_theta=1.2,

                              scaling_ratio=0.1,
                              strong_gravity_mode=False,
                              multithread=False,
                              gravity=10.0)


plt.figure(figsize=(30, 15))
f = dict(nx.degree(sF2))
node_sizes = [v*10 for v in f.values()]
plt.title('Communities found in the Nutritional network')
cmap = cm.get_cmap('viridis', len(np.unique(v)))
fig = plt.gcf()
nx.draw(sF2, positions, node_size=node_sizes, with_labels=False, width = 0.35, node_color=list(p.values()), alpha=0.9)
# add legend
legend=[]
for idx,el in enumerate(cmap.colors):
  legend.append(mpatches.Patch(color=cmap.colors[idx], label=list(communities_named.keys())[idx]+' (size:'+str(size_communities[idx])+')'))
plt.legend(handles=legend)
fig.set_size_inches(20, 15)
plt.savefig('communities_graph')


#World Cloud to better define communities:
wordcloud_dict={}
for i in np.unique(v):
  a=get_keys_from_value(p, i)
  degree_a={}
  for node in a:
      degree_a[node]=sF2.degree(node)
      
  s_dict=dict(sorted(degree_a.items(), key=lambda item: item[1],reverse=True))
  text=[]
  for k,v in s_dict.items():
      text=text+[k]*v
  text_comm = ''
  for el in text:
      text_comm +=el+' '
  wordcloud_dict[i]= text_comm


#Title of the network
names=list(communities_named.keys())


width = 1200; height = 2000
wordcloud_0 = WordCloud(colormap = cm.OrRd, max_words = 100, width=width, height=height, background_color = "white",
                       collocations = False).generate(wordcloud_dict[list(wordcloud_dict.keys())[0]])
wordcloud_1 = WordCloud(colormap = cm.OrRd, max_words = 100, width=width, height=height, background_color = "white",
                       collocations = False).generate(wordcloud_dict[list(wordcloud_dict.keys())[1]])
wordcloud_2 = WordCloud(colormap = cm.OrRd, max_words = 100, width=width, height=height, background_color = "white",
                       collocations = False).generate(wordcloud_dict[list(wordcloud_dict.keys())[2]])
wordcloud_3 = WordCloud(colormap = cm.OrRd, max_words = 100, width=width, height=height, background_color = "white",
                       collocations = False).generate(wordcloud_dict[list(wordcloud_dict.keys())[3]])


fig, axs = plt.subplots(1, 4,figsize=(60,20))
axs[0].imshow(wordcloud_0); axs[0].set_title(names[0],size=40); axs[0].axis('off')
axs[1].imshow(wordcloud_1); axs[1].set_title(names[1],size=40); axs[1].axis('off')
axs[2].imshow(wordcloud_2); axs[2].set_title(names[2],size=40); axs[2].axis('off')
axs[3].imshow(wordcloud_3); axs[3].set_title(names[3],size=40); axs[3].axis('off')
plt.savefig('Communities')
plt.show()


adj_matrix = nx.to_numpy_matrix(sF2) 
node_list = list(sF2.nodes())

#non-deterministic btw
clusters = SpectralClustering(affinity='precomputed', n_clusters=4).fit_predict(adj_matrix)


for i in np.unique(clusters):
  print('Cluster',i,'have',list(clusters).count(i),'elements.')

Cluster 0 have 811 elements.
Cluster 1 have 125 elements.
Cluster 2 have 321 elements.
Cluster 3 have 7424 elements.


def provide_aliments_rich_in_one_feature(feature,number_of_elements=10):
  d={}
  for el in F.edges(feature,data=True):
      d[el[1]]=el[2]['weight']
  features=list(d.keys())
  return features[0:number_of_elements]

provide_aliments_rich_in_one_feature('sodium')


df=df_notnorm.copy()

F= nx.Graph()
for el in list(df):
  F.add_node(el,Role='Feature')
for el in df.index:
  F.add_node(el,Role='Aliment')
  
print("There are " +str(F.number_of_nodes())+" nodes in our network.")


for f in df.columns:
  sorted_df=df.sort_values(by=[f],ascending=False).iloc[:criteria,0]
  for idx,el in enumerate(sorted_df.index):
      F.add_weighted_edges_from([(f,el,sorted_df[idx])])


def draw_subgraph(food,micros_food,list_neigh2):
  new_feat=list(set(list_neigh2) - set(micros_food))
  S=nx.Graph()
  S.add_node(food, Role='Food')
  for el in micros_food:
      S.add_node(el,Role='Existing')

  for el in new_feat:
      S.add_node(el,Role='New')

  print("There are " +str(S.number_of_nodes())+" nodes. Macronutrients values correspond to the content in 100g of the food.")

  for el in micros_food:
      S.add_edge(el,food)
  for el in new_feat:
      S.add_edge(el,food)


  color_map=[]
  degree=[]
  for node in S.nodes:
      if S.nodes[node]['Role']=='Existing':
          color_map.append('#9B149A')
          degree.append(1000)
      elif S.nodes[node]['Role']=='New':
          color_map.append('#157B87')
          degree.append(1000)
      else:
          color_map.append('yellow')
          degree.append(5000)


  options={
      'node_color':color_map,
      'node_size':degree,
      'width':0.4,
      #'font_weight':'bold',
      'with_labels':True
  }
  plt.figure(figsize=(8,4),dpi=800)
  nx.draw(S,**options)
  plt.plot()


def provide_better_substitutes(df_norm,F,food,consider_macros=True,deviation_percentage=10):
  macros=['carbohydrate', 'total_fat', 'protein']
  micros_food=[n for n in nx.neighbors(F, food)]
  # Get features the food is rich in (with weights)
  d={}
  for el in F.edges(food,data=True):
      d[el[1]]=el[2]['weight']
  features=list(d.keys())
  # We need a dataframe normalized
  index_feat=list(df_norm.loc[food,:]).index(max(df_norm.loc[food,:]))
  feature=df_norm.columns[index_feat]
  try:
      neigh= nx.neighbors(F, feature)
  except:
      neigh= nx.neighbors(F, 'calories') # in the hypothethical case there is an error, we go for calories as default
  list_neigh= [n for n in neigh]
  # Get list of nodes connected to this main feature with their corresponding neighbours
  d1={}
  for el in list_neigh:
      d1[el]=[el[1] for el in F.edges(el)]
  # Get subset of aliments that have (at least) the same amount of features as the food inputed
  features=list(d.keys())
  similar_items=[]
  for el in d1:
      if set(features).issubset(set(d1[el])):
          similar_items.append(el)
  if len(similar_items)>1:
      """Consider Macros == True: we filter with this subset of food elements that do not provide
        a substantial amount of variation in the levels of macros"""
      food_macros=[df.loc[food][m] for m in macros]
      aliments=similar_items
      d={}
      for al in aliments:
          mac=[]
          for idx,m in enumerate(macros):
              mac.append(food_macros[idx]-float(df.loc[al][m]))
          d[al]=mac
      if consider_macros==True:
          # Keep the ones that stand in +/- 10 grams of each macros
          bool_filter=[]
          for al in aliments:
              var=True
              for el in d[al]:
                  if abs(el)>deviation_percentage:
                      var=False
              bool_filter.append(var)
          similar_macros = [i for (i, v) in zip(aliments,[el == True for el in bool_filter]) if v]

          if len(similar_macros)>1:
              # Sort a dictionary with the elements that have more features
              d3={}
              for el in similar_macros:
                  d3[el]=len(F.edges(el))
              sorted_d = dict(sorted(d3.items(), key=operator.itemgetter(1),reverse=True))
              # Get elements that have the maximum number of features
              filtered_list = [i for (i, v) in zip(list(sorted_d.keys()),[el == max(sorted_d.values()) for el in list(sorted_d.values())]) if v]
              print('Recommended aliments:')
              for el in filtered_list:
                  print("\033[1m" +"- "+ el + "\033[0m")
                  print(f'with deviations of {np.round(-d[el][0],2):+}g in carbohydrates, {np.round(-d[el][1],2):+}g in total fats and {-np.round(d[el][2],2):+}g in proteins.')
              
                  # Get features that have been considered that before were not
                  list_neigh2= [n for n in nx.neighbors(F, el)]
                  draw_subgraph(el,micros_food,list_neigh2)
              
              return similar_macros,filtered_list
          else:
              # print('To consider macros you should increase the deviation percentage')
              pass # We want to proceed as if consider macros was false
          
      print('--- Suggested aliments might have discrepances in macros levels ---')
      # Sort a dictionary with the elements that have more features
      d3={}
      for el in similar_items:
          d3[el]=len(F.edges(el))
      sorted_d = dict(sorted(d3.items(), key=operator.itemgetter(1),reverse=True))
      # Get elements that have the maximum number of features
      filtered_list = [i for (i, v) in zip(list(sorted_d.keys())
                                       ,[el == max(sorted_d.values()) for el in list(sorted_d.values())]) if v]
      
      print('Recommended aliments:');
      for el in filtered_list:
          print("\033[1m" +"- "+ el + "\033[0m",end='\n')
          print(f'with deviations of {np.round(-d[el][0],2):+}g in carbohydrates, {np.round(-d[el][1],2):+}g in total fats and {np.round(-d[el][2],2):+}g in proteins.',end='\n')
          list_neigh2= [n for n in nx.neighbors(F, el)]
          draw_subgraph(el,micros_food,list_neigh2)
      return similar_items,filtered_list

  else:
      print('According to features in which is rich, it is the best food element you can have.')
      return _,_
      pass


s,f=provide_better_substitutes(df,F,'Fat, mutton tallow',consider_macros=True,deviation_percentage=10)


s,f=provide_better_substitutes(df,F,'Fat, mutton tallow',consider_macros=False,deviation_percentage=10)


s,f=provide_better_substitutes(df,F,'Nuts, pecans',consider_macros=True,deviation_percentage=10)


s,f=provide_better_substitutes(df,F,'Cornstarch',consider_macros=True,deviation_percentage=10)


s,f=provide_better_substitutes(df,F,'Eggplant, raw',consider_macros=False,deviation_percentage=10)


s,f=provide_better_substitutes(df_norm,F,'Cauliflower, raw',consider_macros=False,deviation_percentage=10)

	name	serving_size	calories	total_fat	saturated_fat	cholesterol	sodium	choline	folate	folic_acid	...	fat	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	alcohol	ash	caffeine	theobromine	water
0	Cornstarch	100 g	381	0.1g	NaN	0	9.00 mg	0.4 mg	0.00 mcg	0.00 mcg	...	0.05 g	0.009 g	0.016 g	0.025 g	0.00 mg	0.0 g	0.09 g	0.00 mg	0.00 mg	8.32 g
1	Nuts, pecans	100 g	691	72g	6.2g	0	0.00 mg	40.5 mg	22.00 mcg	0.00 mcg	...	71.97 g	6.180 g	40.801 g	21.614 g	0.00 mg	0.0 g	1.49 g	0.00 mg	0.00 mg	3.52 g
2	Eggplant, raw	100 g	25	0.2g	NaN	0	2.00 mg	6.9 mg	22.00 mcg	0.00 mcg	...	0.18 g	0.034 g	0.016 g	0.076 g	0.00 mg	0.0 g	0.66 g	0.00 mg	0.00 mg	92.30 g
3	Teff, uncooked	100 g	367	2.4g	0.4g	0	12.00 mg	13.1 mg	0	0	...	2.38 g	0.449 g	0.589 g	1.071 g	0	0	2.37 g	0	0	8.82 g
4	Sherbet, orange	100 g	144	2g	1.2g	1mg	46.00 mg	7.7 mg	4.00 mcg	0.00 mcg	...	2.00 g	1.160 g	0.530 g	0.080 g	1.00 mg	0.0 g	0.40 g	0.00 mg	0.00 mg	66.10 g

	name	serving_size	calories	total_fat	saturated_fat	cholesterol	sodium	choline	folate	...	fat	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	ash	water
0	Cornstarch	100.0	381.0	0.1	NaN	0.0	9.0	0.4	0.0	...	0.05	0.009	0.016	0.025	0.0	0.09	8.32
1	Nuts, pecans	100.0	691.0	72.0	6.2	0.0	0.0	40.5	22.0	...	71.97	6.180	40.801	21.614	0.0	1.49	3.52
2	Eggplant, raw	100.0	25.0	0.2	NaN	0.0	2.0	6.9	22.0	...	0.18	0.034	0.016	0.076	0.0	0.66	92.30
3	Teff, uncooked	100.0	367.0	2.4	0.4	0.0	12.0	13.1	0.0	...	2.38	0.449	0.589	1.071	0.0	2.37	8.82
4	Sherbet, orange	100.0	144.0	2.0	1.2	1.0	46.0	7.7	4.0	...	2.00	1.160	0.530	0.080	1.0	0.40	66.10

	calories	total_fat	cholesterol	sodium	carbohydrate	glucose	sugars	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	water
0	381.0	0.1	0.0	9.0	91.27	0.00	0.00	0.009	0.016	0.025	0.0	8.32
1	691.0	72.0	0.0	0.0	13.86	0.04	3.97	6.180	40.801	21.614	0.0	3.52
2	25.0	0.2	0.0	2.0	5.88	1.58	3.53	0.034	0.016	0.076	0.0	92.30
3	367.0	2.4	0.0	12.0	73.13	0.73	1.84	0.449	0.589	1.071	0.0	8.82
4	144.0	2.0	1.0	46.0	30.40	0.00	24.32	1.160	0.530	0.080	1.0	66.10
5	25.0	0.3	0.0	30.0	4.97	0.94	1.91	0.130	0.034	0.031	0.0	92.07
6	42.0	0.7	0.0	3.0	6.70	0.00	3.01	0.151	0.060	0.307	0.0	85.66
7	282.0	23.0	73.0	59.0	0.00	0.00	0.00	10.190	9.600	1.850	73.0	59.47
8	300.0	24.0	72.0	842.0	0.46	0.00	0.46	15.259	7.023	0.724	72.0	51.80
9	290.0	18.0	0.0	490.0	9.00	0.00	0.80	2.849	4.376	9.332	0.0	45.00

	calories	total_fat	cholesterol	sodium	carbohydrate	glucose	sugars	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	water
count	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03	8.789000e+03
mean	-7.404855e-17	-7.015791e-17	3.387601e-15	5.043816e-16	-2.887540e-15	-3.087858e-15	-2.251205e-15	-9.321124e-17	-4.508852e-16	6.913598e-16	3.387601e-15	4.783218e-16
std	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00	1.000057e+00
min	-1.332239e+00	-6.674226e-01	-3.299729e-01	-3.261974e-01	-8.113754e-01	-1.706587e-01	-4.925090e-01	-5.343960e-01	-5.526513e-01	-4.186998e-01	-3.299729e-01	-1.758527e+00
25%	-7.964793e-01	-6.042009e-01	-3.299729e-01	-2.878656e-01	-8.095415e-01	-1.706587e-01	-4.925090e-01	-5.112097e-01	-5.418101e-01	-3.921638e-01	-3.299729e-01	-7.933404e-01
50%	-2.077327e-01	-3.449918e-01	-3.129302e-01	-2.367564e-01	-4.688080e-01	-1.706587e-01	-4.567898e-01	-3.110921e-01	-3.182481e-01	-3.016644e-01	-3.129302e-01	2.914385e-01
75%	6.518375e-01	2.176816e-01	2.239150e-01	9.545283e-02	4.690343e-01	-1.706587e-01	-5.148618e-02	1.151304e-01	1.446982e-01	-4.640469e-02	2.239150e-01	7.750068e-01
max	3.978256e+00	5.654750e+00	2.608622e+01	4.094229e+01	2.856369e+00	3.332451e+01	6.782546e+00	1.434216e+01	1.170795e+01	1.435884e+01	2.608622e+01	1.491260e+00

	name	serving_size	calories	total_fat	saturated_fat	cholesterol	sodium	choline	folate	...	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	ash	water	Cluster num.
0	Cornstarch	100.0	381.0	0.1	4.362579	0.0	9.0	0.4	0.0	...	0.009	0.016	0.025	0.0	0.09	8.32	0
1	Nuts, pecans	100.0	691.0	72.0	6.200000	0.0	0.0	40.5	22.0	...	6.180	40.801	21.614	0.0	1.49	3.52	2
2	Eggplant, raw	100.0	25.0	0.2	0.767811	0.0	2.0	6.9	22.0	...	0.034	0.016	0.076	0.0	0.66	92.30	6
3	Teff, uncooked	100.0	367.0	2.4	0.400000	0.0	12.0	13.1	0.0	...	0.449	0.589	1.071	0.0	2.37	8.82	0
4	Sherbet, orange	100.0	144.0	2.0	1.200000	1.0	46.0	7.7	4.0	...	1.160	0.530	0.080	1.0	0.40	66.10	1
5	Cauliflower, raw	100.0	25.0	0.3	0.100000	0.0	30.0	44.3	57.0	...	0.130	0.034	0.031	0.0	0.76	92.07	6
6	Taro leaves, raw	100.0	42.0	0.7	0.200000	0.0	3.0	12.8	126.0	...	0.151	0.060	0.307	0.0	1.92	85.66	6
7	Lamb, raw, ground	100.0	282.0	23.0	10.000000	73.0	59.0	69.3	18.0	...	10.190	9.600	1.850	73.0	0.87	59.47	1
8	Cheese, camembert	100.0	300.0	24.0	15.000000	72.0	842.0	15.4	62.0	...	15.259	7.023	0.724	72.0	3.68	51.80	1
9	Vegetarian fillets	100.0	290.0	18.0	2.800000	0.0	490.0	82.0	102.0	...	2.849	4.376	9.332	0.0	5.00	45.00	1

Recommendation System of common food and its nutrients based on network graphs.

Recommendation System of common food and its nutrients based on network graphs.

Computational Tools for Data Science

Computational Tools for Data Science

1. Motivation

1.1 Our Dataset:

1.2 Our Goals:

2. Preprocessing and Data Cleaning

2.1 Pre-cleaning of the dataset:

2.1 Dealing with missing values:

2.2 Data Normalization:

3. Creation of the Network

How are the aliments and nutrients connected with each other? ¶

3.1 Visualize the Network:

3.2 Statistical Analysis of the Network:

3.3 Finding Communities:

3.3.1 Analysis of the Communities:

3.3.2 Visualize Communities:

3.3.3 Getting more insight into the existing communities: Generation of World Clouds per community

3.3.4 Spectral Clustering:

4. Recommendation Systems

4.1 Recommendation System based on lack of specific nutrients/features in your diet:

4.2 Recommendation System for better food substitutes based on neighbourhood:

	calories	total_fat	saturated_fat	cholesterol	sodium	choline	folate	folic_acid	niacin	pantothenic_acid	...	fat	saturated_fatty_acids	monounsaturated_fatty_acids	polyunsaturated_fatty_acids	fatty_acids_total_trans	alcohol	ash	caffeine	theobromine	water
name
Cornstarch	0.422395	0.001	0.044448	0.000000	0.000232	0.000166	0.000000	0.0	0.000000	0.000000	...	0.0005	0.000094	0.000191	0.000335	0.000000	0.0	0.000902	0.0	0.0	0.0832
Nuts, pecans	0.766075	0.720	0.063608	0.000000	0.000000	0.016852	0.005811	0.0	0.009153	0.025014	...	0.7197	0.064644	0.487531	0.289643	0.000000	0.0	0.014930	0.0	0.0	0.0352
Eggplant, raw	0.027716	0.002	0.006964	0.000000	0.000052	0.002871	0.005811	0.0	0.005090	0.008145	...	0.0018	0.000356	0.000191	0.001018	0.000000	0.0	0.006613	0.0	0.0	0.9230
Teff, uncooked	0.406874	0.024	0.003128	0.000000	0.000310	0.005451	0.000000	0.0	0.026376	0.027304	...	0.0238	0.004697	0.007038	0.014352	0.000000	0.0	0.023747	0.0	0.0	0.0882
Sherbet, orange	0.159645	0.020	0.011470	0.000323	0.001187	0.003204	0.001057	0.0	0.000494	0.006493	...	0.0200	0.012134	0.006333	0.001072	0.000323	0.0	0.004008	0.0	0.0	0.6610

Recommendation System of common food and its nutrients based on network graphs.

Recommendation System of common food and its nutrients based on network graphs.

Computational Tools for Data Science

Computational Tools for Data Science

1. Motivation

1.1 Our Dataset:

1.2 Our Goals:

2. Preprocessing and Data Cleaning

2.1 Pre-cleaning of the dataset:

2.1 Dealing with missing values:

2.2 Data Normalization:

3. Creation of the Network

How are the aliments and nutrients connected with each other? ¶

3.1 Visualize the Network:

3.2 Statistical Analysis of the Network:

3.3 Finding Communities:

3.3.1 Analysis of the Communities:

3.3.2 Visualize Communities:

3.3.3 Getting more insight into the existing communities: Generation of World Clouds per community

3.3.4 Spectral Clustering:

4. Recommendation Systems

4.1 Recommendation System based on lack of specific nutrients/features in your diet:

Recommend aliments rich in this nutrient.¶

4.2 Recommendation System for better food substitutes based on neighbourhood: